Algorithmic Learning Theory -

Algorithmic Learning Theory

27th International Conference, ALT 2016, Bari, Italy, October 19-21, 2016, Proceedings
Buch | Softcover
XIX, 371 Seiten
2016 | 1st ed. 2016
Springer International Publishing (Verlag)
978-3-319-46378-0 (ISBN)
53,49 inkl. MwSt
This book constitutes the refereed proceedings of the 27th International Conference on Algorithmic Learning Theory, ALT 2016, held in Bari, Italy, in October 2016, co-located with the 19th International Conference on Discovery Science, DS 2016. The 24 regular papers presented in this volume were carefully reviewed and selected from 45 submissions. In addition the book contains 5 abstracts of invited talks. The papers are organized in topical sections named: error bounds, sample compression schemes; statistical learning, theory, evolvability; exact and interactive learning; complexity of teaching models; inductive inference; online learning; bandits and reinforcement learning; and clustering.

Error bounds, sample compression schemes.- Statistical learning, theory, evolvability.- Exact and interactive learning.- Complexity of teaching models.- Inductive inference.- Online learning.- Bandits and reinforcement learning.- Clustering.

Erscheinungsdatum
Reihe/Serie Lecture Notes in Artificial Intelligence
Lecture Notes in Computer Science
Zusatzinfo XIX, 371 p. 21 illus.
Verlagsort Cham
Sprache englisch
Maße 155 x 235 mm
Themenwelt Informatik Datenbanken Data Warehouse / Data Mining
Informatik Theorie / Studium Künstliche Intelligenz / Robotik
Schlagworte Active learning • adversary models • Applications • Artificial Intelligence • artificial intelligence (incl. robotics) • boolean function learning • Clustering • Computer Science • conference proceedings • evolutionary algorithms • Inductive Inference • Informatics • Interactive Learning • Local Search • Models of learning • online learning algorithms • Online learning theory • Optimization • Perceptron • query learning • Reinforcement Learning • Research • Robotics • Sample complexity and generalization bounds • Semi-Supervised Learning • sequential decision making • structured prediction • Unsupervised Learning
ISBN-10 3-319-46378-0 / 3319463780
ISBN-13 978-3-319-46378-0 / 9783319463780
Zustand Neuware
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